The demand for energy and cost efficient buildings has made architects and contractors more aware of the resources consumed by the built environment. While the actual economic and environmental costs of future construction can never be completely predicted, energy simulations and cost modeling have become accepted ways to guide the design and construction process by comparing possible outcomes. These tools are now commonplace in the construction industry, and researchers are continuing to develop new and innovative strategies to optimize building design and construction. Previous research has proven that genetic algorithms are effective methods to evaluate and optimize building design in situations that contain a large number of possible solutions. The technique makes a computationally difficult multi-optimization process possible but is still a reactive and time consuming process that focuses on evaluation rather than solution generation. This research presented in this paper builds upon established multi-objective optimization techniques that use an energy simulator to estimate a conceptual building’s energy use as well as construction cost. The study compares simulations of a simplified model of a 3-story inpatient hospital located in Atlanta, Georgia using a defined set of variables. A combined global minimum of annual energy consumption and total construction is sought after using a method that utilizes a genetic algorithm. The second phase of this research uses a modified approach that combines the traditional genetic algorithm with a seeding method that utilizes previous results. A new set of simulations were established that duplicates the initial trials using a slightly modified set of design variables. The simulation was altered, and the phase one trials were utilized as the first generation of simulated solutions. The objective of this thesis is to explore one method of making energy use and cost estimating more accessible to the construction industry by combining simulation optimization and indexing. The results indicate that this study’s proposed augmented approach has potential benefits to building design optimization, although more research is required to validate this hypothesis in its entirety. This study concludes that the proposed approach can potentially reduce the time needed for individual optimization exercises by creating a cumulative, robust catalog of previous computations that will inform and seed future analyses. The research was conducted in five general stages. The first part defines the research problem and scope of research to be conducted. In the second part, the concepts of genetic algorithms and energy simulation are explored in a comprehensive literature review. The remaining parts explain the trial simulations performed in this study. Part three explains the experiment’s methodology, and part four describes the simulation results. The fifth and final part looks at what the possible conclusions that can be made from analyzing the study’s results.

An energy analysis was performed on the Texas A & M University at Qatar building in Doha, Qatar. The building and its HVAC systems were modeled using EnergyPlus. Building chilled water and electrical data were collected to validate the computer simulation. The simulated monthly electricity consumption was within plus/minus 5 percent of the metered building data. Ninety-five percent of simulated hourly electricity data in a day were within plus/minus 10 percent of metered data. Monthly chilled water demand was within plus/minus 18 percent of measurements, and simulated monthly demand was correlated to metered monthly values with an R-squared correlation coefficient of 0.95. Once the simulation was verified with the metered data, an optimization of the building's HVAC systems was performed. Better utilizing the building's variable speed fans at part loads showed potential annual electricity savings of 16 percent over the base case, with another 22 percent savings in chilled water energy. After converting chilled water savings to equivalent chiller electricity savings, the potential utility cost savings over the base case were found to be $90,000/yr at local utility rates. Reducing outdoor air intake to ASHRAE indoor air quality minimums yielded an additional 17 percent in potential chilled water savings and brought total monetary savings over the base case to $110,000/yr. Using a dedicated outside air system to precisely control individual zone ventilation showed potential for an additional 12 percent chilled water savings and $14,000 in yearly utility savings, while also eliminating cases of under-ventilation. A hypothetical retrofit of fan powered terminal units (FPTU's) resulted in energy savings only at very low minimum flow rates, below ventilation standards. Savings were never more than 20 percent over the no-fan case. Series FPTU's showed no savings at any flow setting and negligible difference was found between ECM and SCR motor control. Finally, the dependence on climate of each improvement was studied. Simulations were run in the relatively milder climates of Houston and Phoenix and compared to those found for Doha. It was found that variable speed fan operation is a more cost effective option for milder climates, while outside air control is more cost effective in extreme hot and humid climates such as Doha. Future study is needed to make the FPTU model valid for different climates and flow ranges.

Turkey, as a recent signatory to the United Nations Framework Convention on Climate Change (UNFCCC) has to adopt policies to restrict greenhouse gas emissions at a time when energy demand is increasing rapidly. We report on an intertemporal, spatial network model representing the energy system that seeks to address the difficult trade-offs involved. We compute and optimal mix of fuels and technologies / considering efficiencies and investments in generation and transmission. The model allows analysis of emissions and investment decisions to attain set targets. Extensions allowing the study of dependency on fossil fuels and imports are also discussed.

This thesis examines an effort to increase energy conservation in low-income housing communities through an educational program. The Saving Green Program offered at Foundation Communities in Austin, Texas attempts to educate residents about their energy usage and ways to reduce it. Activities include a class, an in-home energy visit, and energy feedback reports. We take several approaches in analyzing the program’s impact. First, we conduct a descriptive characterization of participants with regards to income, household makeup, and electricity usage. We then interviewed program participants in order to assess impact and participant reaction. Finally, we conduct two quantitative analyses to measure effectiveness. These include a comparison between groups of participants and non-participants, and a comparison of participants’ electricity usage after the program against their own usage before the program. Our descriptive assessment shows that most in our sample are either single seniors or households with multiple children. Their electricity usage varies however nearly half of load usually goes to cooling and their usage appears to be uncorrelated with income. Load patterns are dictated more by apartment size than anything else. Interviews show that participants readily absorbed and disseminated information regarding plug loads, but had poor understanding of the importance of cooling load. Finally, our quantitative analysis shows, in accordance with the interviews, that participants did not exhibit any systematic change in electricity consumption in summer, however there is some evidence that winter load decreased after the program. / text

abstract: Buildings consume nearly 50% of the total energy in the United States, which drives the need to develop high-fidelity models for building energy systems. Extensive methods and techniques have been developed, studied, and applied to building energy simulation and forecasting, while most of work have focused on developing dedicated modeling approach for generic buildings. In this study, an integrated computationally efficient and high-fidelity building energy modeling framework is proposed, with the concentration on developing a generalized modeling approach for various types of buildings. First, a number of data-driven simulation models are reviewed and assessed on various types of computationally expensive simulation problems. Motivated by the conclusion that no model outperforms others if amortized over diverse problems, a meta-learning based recommendation system for data-driven simulation modeling is proposed. To test the feasibility of the proposed framework on the building energy system, an extended application of the recommendation system for short-term building energy forecasting is deployed on various buildings. Finally, Kalman filter-based data fusion technique is incorporated into the building recommendation system for on-line energy forecasting. Data fusion enables model calibration to update the state estimation in real-time, which filters out the noise and renders more accurate energy forecast. The framework is composed of two modules: off-line model recommendation module and on-line model calibration module. Specifically, the off-line model recommendation module includes 6 widely used data-driven simulation models, which are ranked by meta-learning recommendation system for off-line energy modeling on a given building scenario. Only a selective set of building physical and operational characteristic features is needed to complete the recommendation task. The on-line calibration module effectively addresses system uncertainties, where data fusion on off-line model is applied based on system identification and Kalman filtering methods. The developed data-driven modeling framework is validated on various genres of buildings, and the experimental results demonstrate desired performance on building energy forecasting in terms of accuracy and computational efficiency. The framework could be easily implemented into building energy model predictive control (MPC), demand response (DR) analysis and real-time operation decision support systems. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2016

abstract: Residential air conditioning systems represent a critical load for many electric utilities, especially for those who serve customers in hot climates. In hot and dry climates, in particular, the cooling load is usually relatively low during night hours and early mornings and hits its maximum in the late afternoon. If electric loads could be shifted from peak hours (e.g., late afternoon) to off-peak hours (e.g., late morning), not only would building operation costs decrease, the need to run peaker plants, which typically use more fossil fuels than non-peaker plants, would also decrease. Thus, shifting electricity consumption from peak to off-peak hours promotes economic and environmental savings. Operational and technological strategies can reduce the load during peak hours by shifting cooling operation from on-peak hours to off-peak hours. Although operational peak load shifting strategies such as precooling may require mechanical cooling (e.g., in climates like Phoenix, Arizona), this cooling is less expensive than on-peak cooling due to demand charges or time-based price plans. Precooling is an operational shift, rather than a technological one, and is thus widely accessible to utilities’ customer base. This dissertation compares the effects of different precooling strategies in a Phoenix-based utility’s residential customer market and assesses the impact of technological enhancements (e.g., energy efficiency measures and solar photovoltaic system) on the performance of precooling. This dissertation focuses on the operational and technological peak load shifting strategies that are feasible for residential buildings and discusses the advantages of each in terms of peak energy savings and residential electricity cost savings. / Dissertation/Thesis / Doctoral Dissertation Civil Engineering 2016

Several studies have been conducted and revealed that household is the major energy consumer sector in developing countries like Ethiopia. This study focuses on evaluation of the existing residential energy consumption and projection of the energy demand.   The energy consumption has been studied by conducting survey on 350 households using stratified random sampling technique. Then the analyzed data have been used to model the energy demand and to project the future energy consumption till 2030 using LEAP (Long Range Energy Alternative Planning) simulation software.   The model is done based upon baseline scenario and energy efficiency improvement scenario (mitigation scenario). The total energy consumption in Bahir dar in 2014 is nearly 330 Giga watt hour per year, and of this value about 83.8% is used for cooking and TV, lighting, refrigerator, and water heater consume 7%, 4.5%, 3.5%, 1%  of the total energy and remaining 0.2% is consumed for other auxiliary appliances. The projection of the energy consumption in 2030 will be more 450 Giga-watt-hour with business as usual scenario compared to just less than 350 Giga-watt-hour with mitigation scenario. As the result of the poor consumption efficiency, households that use traditional biomass tend to have more primary energy intensity than household that use electricity. The consumption of electricity is projecting rapidly while charcoal and firewood will still be the significant energy sources. The potential for energy saving is from improving the efficiency of stoves. Comparing with developed countries, for example Sweden, where the energy in dwellings is mostly used for space and water heating and the energy saving mostly target on improving wall insulations, the energy saving on Bahir dar is based mostly on cooking. The findings obtained in this shows options to improve household energy efficiency intervention planning and to enhance the effectiveness of policy interventions. Further studies could be done on modeling of other sectors.

The purpose of this paper is to explore the ferrocement building technique for sustainable housing. Ferrocement involves the use of conventional cement with fine aggregate and several layers of steel, with the advantage of higher strength than conventional reinforced concrete, limited formwork and thinner sections. It is particularly suitable for thin shell structures, where geometry minimizes bending loads. Architectural flexibility is one of the main priorities considered in sustainable housing, along with energy efficiency, occupant comfort, resistance to seismic and tornado events, affordability and durability. Ferrocement’s historical and present applications are covered, along with other building techniques, in order to establish best practices and possible improvements. Reducing construction labor is a particular focus, which has limited ferrocement development in recent years. Computer modeling of shell form finding is described, with three case studies created. A structural analysis method is described and applied to each case study to verify general building code safety. Energy modeling is performed in two climates for each case study in the United States and compared to key PassivHaus energy demand limits. Net zero energy use is possible with on-site solar photovoltaic generation.

Energy conservation and efficiency are often widely touted as non-controversial, cost-positive methods of reducing energy consumption and its associated environmental effects.  However, past programs to encourage residential energy efficiency and conservation have failed to make an impact.  A growing amount of research identifies energy feedback as a method to provide consumers with the information and motivation necessary to make appropriate energy-saving decisions.  JouleBug is a social, playful, mobile smartphone application designed to help users in the U.S. reduce energy consumption and live sustainably through behavioral changes.   This project initiated the design of an energy feedback system for JouleBug that provides estimates of a user’s energy savings for completing 38 residential energy saving actions.  Mathematical models were developed to estimate JouleBug users’ energy savings for each of the energy saving actions, based on 13 input parameters. A method was developed to aggregate each of the savings actions across various energy end-uses into a summary of the user’s energy savings over a given time period.  Additionally, the energy models were utilized to analyze an average user’s potential energy, cost, and greenhouse gas savings over a year. Research into the design components of effective feedback systems was applied in the context of JouleBug to compliment the engineering work.  The components of frequency, measurement unit, data granularity, recommended actions, and comparisons were examined.  Design suggestions based on these components that utilized the energy models to provide effective energy feedback to JouleBug’s users were proposed.  Finally, this report describes opportunities for future research using simple energy modeling methods to provide effective consumer energy feedback in a mobile smartphone application.

Massive parallelism combined with complex memory hierarchies and heterogeneity in high-performance computing (HPC) systems form a barrier to efficient application and architecture design. The performance achievements of the past must continue over the next decade to address the needs of scientific simulations. However, building an exascale system by 2022 that uses less than 20 megawatts will require significant innovations in power and performance efficiency. A key limitation of past approaches is a lack of power-performance policies allowing users to quantitatively bound the effects of power management on the performance of their applications and systems. Existing controllers and predictors use policies fixed by a knowledgeable user to opportunistically save energy and minimize performance impact. While the qualitative effects are often good and the aggressiveness of a controller can be tuned to try to save more or less energy, the quantitative effects of tuning and setting opportunistic policies on performance and power are unknown. In other words, the controller will save energy and minimize performance loss in many cases but we have little understanding of the quantitative effects of controller tuning. This makes setting power-performance policies a manual trial and error process for domain experts and a black art for practitioners. To improve upon past approaches to high-performance power management, we need to quantitatively understand the effects of power and performance at scale. In this work, I have developed theories and techniques to quantitatively understand the relationship between power and performance for high performance systems at scale. For instance, our system-level, iso-energy-efficiency model analyzes, evaluates and predicts the performance and energy use of data intensive parallel applications on multi-core systems. This model allows users to study the effects of machine and application dependent characteristics on system energy efficiency. Furthermore, this model helps users isolate root causes of energy or performance inefficiencies and develop strategies for scaling systems to maintain or improve efficiency.  I have also developed methodologies which can be extended and applied to model modern heterogeneous architectures such as GPU-based clusters to improve their efficiency at scale. / Ph. D.